How to optimize the construction of a histogram on the GPU?
Best insight from top research papers
To optimize the construction of a histogram on the GPU, several techniques can be employed based on the research findings. One approach involves implementing efficient GPU-based algorithms that utilize parallelism to distribute image processing tasks among GPU devices . Additionally, utilizing multi-core CPU and many-core GPU computing can significantly enhance the speed of histogram computations, with GPU-optimized streaming histograms showing a 7x speedup compared to multi-threaded CPU implementations . Furthermore, techniques such as automatically optimizing histogram computation codes for GPUs using OpenACC can improve performance portability by exploiting multiple copies of histograms for acceleration . By balancing conflict rates, memory footprint, and leveraging hardware-supported atomic operations, optimized histogram implementations can outperform existing primitives and hand-written codes, even for specialized datasets .
Answers from top 5 papers
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| Papers (5) | Insight |
|---|---|
01 Feb 2016 | An OpenACC optimizer automates rewriting histogram computation code on a GPU, utilizing multiple copies of histograms to enhance performance portability and maximize GPU efficiency. |
26 Sep 2020 | The paper proposes an efficient GPU-based algorithm utilizing three-stage parallelisms to optimize histogram construction, achieving significantly faster processing speeds compared to conventional CPU-based methods. |
| Optimize histogram construction on GPU by utilizing CUDA with multi-core CPU and many-core GPU computing, fine-tuning for different architectural platforms to increase parallelism. | |
| Optimize histogram construction on GPU by employing work-efficient techniques, supporting various operators, and utilizing hardware atomic operations efficiently, as demonstrated in CUDA library and Futhark language extensions. | |
| Optimize histogram construction on GPU by partitioning input image into patches, counting pixels for each grayscale value in parallel, and combining output patches into a composite image. |
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